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Record W2473427461 · doi:10.1016/j.proeng.2016.06.295

Sprint Canoe Blade Hydrodynamics - Modeling and On-water Measurement

2016· article· en· W2473427461 on OpenAlexaff
Dana Morgoch, Cameron Galipeau, Stephen Tullis

Bibliographic record

VenueProcedia Engineering · 2016
Typearticle
Languageen
FieldEngineering
TopicSports Dynamics and Biomechanics
Canadian institutionsMcMaster University
Fundersnot available
KeywordsBlade (archaeology)SprintPressure measurementBlade element momentum theoryMechanicsVortexStructural engineeringEngineeringBlade element theoryMarine engineeringStrain gaugeRotor (electric)SimulationAerospace engineeringMechanical engineeringPhysicsTurbine blade

Abstract

fetched live from OpenAlex

A computational fluid dynamics model of the transient flow around a sprint canoe blade has been developed including the full blade motion in the catch and draw phases of the stroke, with the translational and rotational path of the blade is obtained from video analysis of a national team athlete. Examination of the blade path and associated flow patterns around the blade reveals the development of tip and side vortices and their interaction with the blade. An interval of reversed flow and pressure at the top of the blade late in the catch is seen and results in a braking pressure field on the blade surface. On-water measurements have also been made using a new instrumented paddle with multiple strain gauge full bridges. This level of bending moment measurement then allows the tracking of the real centre of pressure of the blade force and the determination of the real blade force (and its components) through the stroke.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.008
GPT teacher head0.150
Teacher spread0.143 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations7
Published2016
Admission routes1
Has abstractyes

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